A Deep Model with Shape-Preserving Loss for Gland Instance Segmentation
A Deep Model with Shape-Preserving Loss for Gland Instance Segmentation
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DOI：
10.1007/978-3-030-00934-2_16
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发表时间：
2018-09
期刊：
影响因子：
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通讯作者：
Zengqiang Yan;Xin Yang;K. Cheng
中科院分区：
文献类型：
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作者：
Zengqiang Yan;Xin Yang;K. Cheng
在组织学图像中分割腺体实例不仅需要将腺体从复杂的背景中分离出来，而且需要通过精确的边界检测来单独识别每个腺体。这是一项非常具有挑战性的任务，因为来自背景的大量噪声，相邻腺体之间的微小间隙，以及由粘附腺体实例引起的“聚结”问题。现有方法采用多通道/多任务深度模型分别完成逐像素腺体分割和边界检测，模型复杂度高，训练难度大。在本文中，我们提出了一种具有新的形状保持损失的统一深度模型，该模型可以同时用于像素腺体分割和边界检测的训练。所提出的形状保持损失有助于显著降低模型复杂度，使训练过程更具可控性。与目前最先进的方法相比，该模型在2015年MICCAI Gland Challenge数据集上取得了最佳的综合性能。此外，将所提出的形状保持损失集成到任何基于学习的医学图像分割网络中的灵活性为进一步提高其他应用的性能提供了巨大的潜力。
Segmenting gland instance in histology images requires not only separating glands from a complex background but also identifying each gland individually via accurate boundary detection. This is a very challenging task due to lots of noises from the background, tiny gaps between adjacent glands, and the “coalescence” problem arising from adhesive gland instances. State-of-the-art methods adopted multi-channel/multi-task deep models to separately accomplish pixel-wise gland segmentation and boundary detection, yielding a high model complexity and difficulties in training. In this paper, we present a unified deep model with a new shape-preserving loss which facilities the training for both pixel-wise gland segmentation and boundary detection simultaneously. The proposed shape-preserving loss helps significantly reduce the model complexity and make the training process more controllable. Compared with the current state-of-the-art methods, the proposed deep model with the shape-preserving loss achieves the best overall performance on the 2015 MICCAI Gland Challenge dataset. In addition, the flexibility of integrating the proposed shape-preserving loss into any learning based medical image segmentation networks offers great potential for further performance improvement of other applications.